Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/4417#discussion_r31484689
--- Diff: examples/src/main/python/broadcast.py ---
@@ -0,0 +1,60 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import sys
+import time
+from operator import add
+from pyspark import SparkContext, SparkConf
+
+# Broadcast variables allow the programmer to keep a read-only variable
+# cached on each machine rather than shipping a copy of it with tasks.
+# Spark also attempts to distribute broadcast variables using efficient
+# broadcast algorithms to reduce communication cost.
+
+# Usage: BroadcastTest [slices] [numElem] [broadcastAlgo] [blockSize]
+
+if __name__ == "__main__":
+ slices = int(sys.argv[0]) if len(sys.argv) > 1 else 1
+ num = int(sys.argv[1]) if len(sys.argv) > 2 else 1000000
+ bcName = sys.argv[2] if len(sys.argv) > 3 else "Http"
+ blockSize = sys.argv[3] if len(sys.argv) > 4 else "4092"
+
+ conf = SparkConf().setAppName("Broadcast Test") \
+ .setMaster("local") \
+ .set("spark.broadcast.factory",
"org.apache.spark.broadcast.%sBroadcastFactory" % bcName) \
+ .set("spark.broadcast.blockSize", blockSize)
+
+ sc = SparkContext(conf=conf)
+ # large broadcast,using broadcast will cost less time!
+ barr1 = sc.broadcast(range(num))
+ for i in range(3):
+ start = time.time()
+ # variable barr1 cached on each machine rather than shipping a
copy of
+ # it with tasks threes times
+ broadcast_result = sc.parallelize(range(10), slices)
+ broadcast_result.map(lambda x: len(barr1.value)).collect()
+ end = time.time()
+ print "Using broadcast: Iteration %s cost time %s" % (i, end-start)
+ # it will cost time
--- End diff --
PySpark will create broadcast object automatically, so this will not have
much difference (each run will create a new broadcast).
I'd like to remove these.
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